Customer Churn Analysis

The story Behind The Data

A bank is concerned that more and more customers are leaving its credit card services. They would really appreciate if someone could analyze it for them, in order to understand the main reasons for leaving the services, and to come up with recommendations for how the bank can mitigate that. Eventually, the bank would like to proactively implement these recommendations in order to keep their customers happy.

A full ERD can be found here

Data Description

In this task, few datasets are provided:

1. BankChurners.csv - this file contains basic information about each client (10 columns). The columns are:

   • CLIENTNUM - Client number. Unique identifier for the customer holding the account;
   • Attrition Flag - Internal event (customer activity) variable - if the client had churned (attrited) or not (existing).
   • Dependent Count - Demographic variable - Number of dependents
   • Card_Category - Product Variable - Type of Card (Blue, Silver, Gold, Platinum)
   • Months_on_book - Period of relationship with bank
   • Months_Inactive_12_mon - No. of months inactive in the last 12 months
   • Contacts_Count_12_mon - No. of Contacts in the last 12 months
   • Credit_Limit - Credit Limit on the Credit Card
   • Avg_Open_To_Buy - Open to Buy Credit Line (Average of last 12 months)
   • Avg_Utilization_Ratio - Average Card Utilization Ratio

2. basic_client_info.csv - this file contains some basic client info per each client

(6 columns) - - CLIENTNUM - Client number. Unique identifier for the customer holding the account - Customer Age - Demographic variable - Customer's Age in Years - Gender - Demographic variable - M=Male, F=Female - Education_Level - Demographic variable - Educational Qualification of the account holder (example: high school, college graduate, etc.-Marital_Status- Demographic variable - Married, Single, Divorced, Unknown -Income_Category` - Demographic variable - Annual Income Category of the account holder (< $40K, $40K - 60K, $60K - $80K, $80K-$120K, > $120K, Unknown)

3. enriched_churn_data.csv - this file contains some enriched data about each client (7 columns) -
   • CLIENTNUM - Client number. Unique identifier for the customer holding the account
   • Total_Relationship_Count - Total no. of products held by the customer
   • Total_Revolving_Bal - Total Revolving Balance on the Credit Card
   • Total_Amt_Chng_Q4_Q1 - Change in Transaction Amount (Q4 over Q1)
   • Total_Trans_Amt - Total Transaction Amount (Last 12 months)
   • Total_Trans_Ct - Total Transaction Count (Last 12 months)
   • Total_Ct_Chng_Q4_Q1 - Change in Transaction Count (Q4 over Q1)

# import necessary libraries
import csv
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import xgboost as xgb
import plotly.express as px
import plotly.io as pio
import plotly.graph_objs as go
pio.renderers.default = 'notebook'
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.model_selection import cross_val_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score

Data wrangling

# import from csv
data0 = pd.read_csv('bankchurners.csv')
data1 = pd.read_csv('enriched_churn_data.csv')
data2 = pd.read_csv('basic_client_info.csv')

# merge all tables
data01 = pd.merge(data0, data1, on='clientnum')
data = pd.merge(data01, data2, on='clientnum')
df = pd.DataFrame(data=data)

pd.set_option('max_colwidth', 10)
pd.set_option('display.max_columns', 21)
df.sample(10)

	clientnum	attrition_flag	dependent_count	card_category	months_on_book	months_inactive_12_mon	contacts_count_12_mon	credit_limit	avg_open_to_buy	avg_utilization_ratio	total_relationship_count	total_revolving_bal	total_amt_chng_q4_q1	total_trans_amt	total_ct_chng_q4_q1	total_trans_ct	customer_age	gender	education_level	marital_status	income_category
5496	717395733	Existi...	3	Blue	36	4	3	4142.0	3454.0	0.166	4	688	0.717	4512	0.976	81	42	F	Doctorate	Married	$40K -...
3753	779348358	Attrit...	1	Blue	37	3	4	1717.0	1717.0	0.000	5	0	1.028	3004	0.586	46	52	F	Uneduc...	Single	Less t...
9450	771190083	Existi...	3	Blue	23	2	1	2410.0	705.0	0.707	6	1705	0.798	2505	0.568	69	36	F	Post-G...	Single	Less t...
7374	789430083	Existi...	5	Blue	32	1	2	14987.0	13562.0	0.095	6	1425	0.800	4470	0.844	83	41	F	High S...	Single	$40K -...
815	716123283	Existi...	1	Blue	48	1	2	7288.0	7288.0	0.000	3	0	0.640	14873	0.714	120	59	M	Unknown	Single	$60K -...
2623	716769783	Existi...	2	Blue	40	2	4	5104.0	4368.0	0.144	3	736	0.679	3943	0.696	78	48	F	Graduate	Married	Unknown
9763	709858533	Existi...	2	Blue	20	1	2	2462.0	441.0	0.821	4	2021	0.966	2320	0.676	57	29	M	Graduate	Divorced	Less t...
2597	780717483	Existi...	3	Blue	40	3	3	7540.0	6767.0	0.103	6	773	0.688	4755	0.706	58	51	F	High S...	Married	Less t...
2170	807971658	Existi...	3	Blue	42	2	3	3669.0	1152.0	0.686	1	2517	0.830	13931	0.836	112	47	F	Unknown	Married	Less t...
4148	710406633	Attrit...	3	Blue	36	3	2	3664.0	3664.0	0.000	3	0	0.839	1168	0.409	31	51	F	Uneduc...	Married	Unknown

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 10127 entries, 0 to 10126
Data columns (total 21 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   clientnum                 10127 non-null  int64  
 1   attrition_flag            10127 non-null  object 
 2   dependent_count           10127 non-null  int64  
 3   card_category             10127 non-null  object 
 4   months_on_book            10127 non-null  int64  
 5   months_inactive_12_mon    10127 non-null  int64  
 6   contacts_count_12_mon     10127 non-null  int64  
 7   credit_limit              10127 non-null  float64
 8   avg_open_to_buy           10127 non-null  float64
 9   avg_utilization_ratio     10127 non-null  float64
 10  total_relationship_count  10127 non-null  int64  
 11  total_revolving_bal       10127 non-null  int64  
 12  total_amt_chng_q4_q1      10127 non-null  float64
 13  total_trans_amt           10127 non-null  int64  
 14  total_ct_chng_q4_q1       10127 non-null  float64
 15  total_trans_ct            10127 non-null  int64  
 16  customer_age              10127 non-null  int64  
 17  gender                    10127 non-null  object 
 18  education_level           10127 non-null  object 
 19  marital_status            10127 non-null  object 
 20  income_category           10127 non-null  object 
dtypes: float64(5), int64(10), object(6)
memory usage: 1.7+ MB

Data Cleaning

#make a copy
df_clean = df.copy()

#check duplicates
df_clean.duplicated().sum()

0

#check nulls
df_clean.attrition_flag.isna().sum()

0

## Pre-processing

cat_cols = [c for c in df_clean.columns if df_clean[c].dtype == 'object']
cat_cols               

['attrition_flag',
 'card_category',
 'gender',
 'education_level',
 'marital_status',
 'income_category']

num_cols = [n for n in df_clean.columns if df_clean[n].dtype != 'object']
num_cols  

['clientnum',
 'dependent_count',
 'months_on_book',
 'months_inactive_12_mon',
 'contacts_count_12_mon',
 'credit_limit',
 'avg_open_to_buy',
 'avg_utilization_ratio',
 'total_relationship_count',
 'total_revolving_bal',
 'total_amt_chng_q4_q1',
 'total_trans_amt',
 'total_ct_chng_q4_q1',
 'total_trans_ct',
 'customer_age']

df_clean[num_cols].describe()

	clientnum	dependent_count	months_on_book	months_inactive_12_mon	contacts_count_12_mon	credit_limit	avg_open_to_buy	avg_utilization_ratio	total_relationship_count	total_revolving_bal	total_amt_chng_q4_q1	total_trans_amt	total_ct_chng_q4_q1	total_trans_ct	customer_age
count	1.0127...	10127....	10127....	10127....	10127....	10127....	10127....	10127....	10127....	10127....	10127....	10127....	10127....	10127....	10127....
mean	7.3917...	2.346203	35.928409	2.341167	2.455317	8631.9...	7469.1...	0.274894	3.812580	1162.8...	0.759941	4404.0...	0.712222	64.858695	46.325960
std	3.6903...	1.298908	7.986416	1.010622	1.106225	9088.7...	9090.6...	0.275691	1.554408	814.98...	0.219207	3397.1...	0.238086	23.472570	8.016814
min	7.0808...	0.000000	13.000000	0.000000	0.000000	1438.3...	3.000000	0.000000	1.000000	0.000000	0.000000	510.00...	0.000000	10.000000	26.000000
25%	7.1303...	1.000000	31.000000	2.000000	2.000000	2555.0...	1324.5...	0.023000	3.000000	359.00...	0.631000	2155.5...	0.582000	45.000000	41.000000
50%	7.1792...	2.000000	36.000000	2.000000	2.000000	4549.0...	3474.0...	0.176000	4.000000	1276.0...	0.736000	3899.0...	0.702000	67.000000	46.000000
75%	7.7314...	3.000000	40.000000	3.000000	3.000000	11067....	9859.0...	0.503000	5.000000	1784.0...	0.859000	4741.0...	0.818000	81.000000	52.000000
max	8.2834...	5.000000	56.000000	6.000000	6.000000	34516....	34516....	0.999000	6.000000	2517.0...	3.397000	18484....	3.714000	139.00...	73.000000

df_clean.attrition_flag.value_counts()  #below the anual churning rate?

Existing Customer    8500
Attrited Customer    1627
Name: attrition_flag, dtype: int64

# Define a dictionary mapping the categories to binary values
dict_ch = {
    'Existing Customer': 0,
    'Attrited Customer': 1,
}

# Use the map() method to replace the values in the 'attrition_flag' column
df_clean['churned'] = df_clean['attrition_flag'].map(dict_ch)

# Drop the original 'attrition_flag' column
df_clean.drop('attrition_flag', axis=1, inplace=True)

df_clean

	clientnum	dependent_count	card_category	months_on_book	months_inactive_12_mon	contacts_count_12_mon	credit_limit	avg_open_to_buy	avg_utilization_ratio	total_relationship_count	total_revolving_bal	total_amt_chng_q4_q1	total_trans_amt	total_ct_chng_q4_q1	total_trans_ct	customer_age	gender	education_level	marital_status	income_category	churned
0	806160108	1	Blue	56	2	3	3193.0	676.0	0.788	2	2517	1.831	1336	1.143	30	61	M	High S...	Married	$40K -...	0
1	804424383	1	Blue	56	3	2	10215.0	9205.0	0.099	3	1010	0.843	1904	1.000	40	63	M	Unknown	Married	$60K -...	0
2	708300483	0	Blue	56	4	3	7882.0	7277.0	0.077	5	605	1.052	704	0.143	16	66	F	Doctorate	Married	Unknown	1
3	808284783	1	Blue	56	0	0	1438.3	1438.3	0.000	6	0	0.813	1951	1.095	44	62	F	Unknown	Married	Less t...	0
4	712720158	1	Blue	56	2	3	13860.0	12208.0	0.119	5	1652	1.255	1910	1.909	32	68	M	Graduate	Married	Unknown	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
10122	714082308	3	Blue	13	1	3	8656.0	8656.0	0.000	2	0	0.893	8056	0.935	89	39	M	High S...	Single	$80K -...	1
10123	710751033	1	Blue	13	1	3	4829.0	4829.0	0.000	1	0	0.836	8286	0.857	65	31	F	Graduate	Single	Less t...	1
10124	780118158	0	Blue	13	2	4	3585.0	1165.0	0.675	2	2420	0.812	15656	0.746	124	27	M	Graduate	Single	Less t...	0
10125	779082633	2	Blue	13	2	3	4107.0	3128.0	0.238	3	979	0.647	14596	0.733	104	30	M	Uneduc...	Single	$60K -...	0
10126	788633733	3	Silver	13	3	2	28174.0	27001.0	0.042	4	1173	0.779	16258	0.714	108	36	M	Graduate	Married	$60K -...	0

10127 rows × 21 columns

# Calculate the percentage of churned customers
num_churned = df_clean[df_clean["churned"] == 1].shape[0]
num_total = df_clean.shape[0]
percent_churned = (num_churned / num_total) * 100

# Format the result as a percentage
print("Percentage of churned customers: {:.2f}%".format(percent_churned))

Percentage of churned customers: 16.07%

Observations

The annual churn rate for credit cards can vary greatly depending on the type of card and the target demographic. On average, it is common to see an annual churn rate of around 20% for credit cards. However, some high-end, premium credit cards may have a lower churn rate, while cards targeted towards subprime borrowers may have a higher churn rate.

It's important to note that a lower churn rate is typically seen as a positive sign for a credit card issuer, as it indicates that customers are satisfied with the card and less likely to switch to a different one. On the other hand, a higher churn rate can indicate that customers are not satisfied with the card or are switching to a different one with more attractive terms.

A preliminary overview of the dataset shows that around 83.93% of customers are active, while 16.07% have churned.

#Convert 'attrition_flag'to boolean dtype
dict_g = {
    'F': ['Female'],
    'M': [' Male'],
}
df_clean['gender'] = df_clean['gender']
df_clean.replace({"gender": dict_g}, inplace=True)

df.describe(include='object')

	attrition_flag	card_category	gender	education_level	marital_status	income_category
count	10127	10127	10127	10127	10127	10127
unique	2	4	2	7	4	6
top	Existi...	Blue	F	Graduate	Married	Less t...
freq	8500	9436	5358	3128	4687	3561

df_clean.describe().loc[['min', 'max', 'mean']].round(2)

	clientnum	dependent_count	months_on_book	months_inactive_12_mon	contacts_count_12_mon	credit_limit	avg_open_to_buy	avg_utilization_ratio	total_relationship_count	total_revolving_bal	total_amt_chng_q4_q1	total_trans_amt	total_ct_chng_q4_q1	total_trans_ct	customer_age	churned
min	7.0808...	0.00	13.00	0.00	0.00	1438.30	3.00	0.00	1.00	0.00	0.00	510.00	0.00	10.00	26.00	0.00
max	8.2834...	5.00	56.00	6.00	6.00	34516.00	34516.00	1.00	6.00	2517.00	3.40	18484.00	3.71	139.00	73.00	1.00
mean	7.3917...	2.35	35.93	2.34	2.46	8631.95	7469.14	0.27	3.81	1162.81	0.76	4404.09	0.71	64.86	46.33	0.16

df_clean.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 10127 entries, 0 to 10126
Data columns (total 21 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   clientnum                 10127 non-null  int64  
 1   dependent_count           10127 non-null  int64  
 2   card_category             10127 non-null  object 
 3   months_on_book            10127 non-null  int64  
 4   months_inactive_12_mon    10127 non-null  int64  
 5   contacts_count_12_mon     10127 non-null  int64  
 6   credit_limit              10127 non-null  float64
 7   avg_open_to_buy           10127 non-null  float64
 8   avg_utilization_ratio     10127 non-null  float64
 9   total_relationship_count  10127 non-null  int64  
 10  total_revolving_bal       10127 non-null  int64  
 11  total_amt_chng_q4_q1      10127 non-null  float64
 12  total_trans_amt           10127 non-null  int64  
 13  total_ct_chng_q4_q1       10127 non-null  float64
 14  total_trans_ct            10127 non-null  int64  
 15  customer_age              10127 non-null  int64  
 16  gender                    10127 non-null  object 
 17  education_level           10127 non-null  object 
 18  marital_status            10127 non-null  object 
 19  income_category           10127 non-null  object 
 20  churned                   10127 non-null  int64  
dtypes: float64(5), int64(11), object(5)
memory usage: 1.7+ MB

corr_matrix = df_clean.corr(numeric_only=True)

fig = px.imshow(corr_matrix,
color_continuous_scale='YlOrRd',
aspect="auto",
title='Correlation Matrix')
fig.update_traces(
text=corr_matrix.round(2),
texttemplate="%{text:.2f}",
textfont_size=12,
hovertemplate=
"Feature 1: %{y}<br>Feature 2: %{x}<br>Correlation: %{text:.2f}<extra></extra>",
customdata=np.moveaxis(corr_matrix.values, 0, -1))
fig.update_xaxes(tickangle=90, tickfont=dict(size=12), title="")
fig.update_yaxes(tickfont=dict(size=12), title="")
fig.update_coloraxes(showscale=True, colorbar=dict(len=0.4, y=0.75))
fig.show()

# filter for churned
df_churned = df_clean[df_clean['churned'] == 1]

corr_matrix = df_churned.corr(numeric_only=True)

fig = px.imshow(corr_matrix,
                color_continuous_scale='YlOrRd',
                aspect="auto",
                title='Correlation Matrix for Churned')
fig.update_traces(
    text=corr_matrix.round(2),
    texttemplate="%{text:.2f}",
    textfont_size=12,
    hovertemplate=
    "Feature 1: %{y}<br>Feature 2: %{x}<br>Correlation: %{text:.2f}<extra></extra>",
    customdata=np.moveaxis(corr_matrix.values, 0, -1))
fig.update_xaxes(tickangle=90, tickfont=dict(size=12), title="")
fig.update_yaxes(tickfont=dict(size=12), title="")
fig.update_coloraxes(showscale=True, colorbar=dict(len=0.4, y=0.75))
fig.show()

X = df_clean.drop('churned', axis=1)
y = df_clean['churned']

# One-hot encode categorical variables
X_encoded = pd.get_dummies(X,
                           columns=[
                               'card_category', 'gender', 'education_level',
                               'marital_status', 'income_category'
                           ])

# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_encoded,
                                                    y,
                                                    test_size=0.2,
                                                    random_state=42)

# Train the XGBoost model
xgb_model = xgb.XGBClassifier()
xgb_model.fit(X_train, y_train)

# Evaluate the model on the testing set
y_pred = xgb_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
confusion_mat = confusion_matrix(y_test, y_pred)

print('Accuracy:', accuracy)
print('Confusion matrix:', confusion_mat)

# Visualize the model's feature importances
importances = pd.Series(xgb_model.feature_importances_,
                        index=X_encoded.columns)
importances_nonzero = importances[importances != 0].sort_values(ascending=True)

# Create a horizontal bar chart of feature importances
fig = go.Figure(
    go.Bar(x=importances_nonzero.values,
           y=importances_nonzero.index,
           orientation='h'))

fig.update_layout(title='Feature Importances (XGBoost)',
                  xaxis_title='F score',
                  yaxis_title='Feature')

fig.show()

Accuracy: 0.9772951628825272
Confusion matrix: [[1701   19]
 [  27  279]]

# Retrain the XGBoost model with the most important features
important_features = importances_nonzero.tail(10).index.tolist()
X_important = X_encoded[important_features]
X_train_important, X_test_important, y_train, y_test = train_test_split(
    X_important, y, test_size=0.2, random_state=42)
xgb_model_important = xgb.XGBClassifier()
xgb_model_important.fit(X_train_important, y_train)

# Evaluate the model on the testing set
y_pred = xgb_model_important.predict(X_test_important)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
confusion_mat = confusion_matrix(y_test, y_pred)

print('Accuracy:', accuracy)
print('Precision:', precision)
print('Recall:', recall)
print('F1 Score:', f1)
print('Confusion matrix:', confusion_mat)

Accuracy: 0.9763079960513327
Precision: 0.9358108108108109
Recall: 0.9052287581699346
F1 Score: 0.920265780730897
Confusion matrix: [[1701   19]
 [  29  277]]

# Create an XGBoost classifier with default hyperparameters
clf = xgb.XGBClassifier()

# Perform 5-fold cross-validation on the data
scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='f1')

# Print the average F1 score across all folds
print('Average F1 score:', scores.mean())

Average F1 score: 0.8996300666012841

# initialize the logistic regression model
lr_model = LogisticRegression()

# train the model on the training data
lr_model.fit(X_train, y_train)

# make predictions on the testing data
y_pred = lr_model.predict(X_test)

# evaluate the model's performance
print(classification_report(y_test, y_pred, zero_division=1))

              precision    recall  f1-score   support

           0       0.85      1.00      0.92      1720
           1       1.00      0.00      0.00       306

    accuracy                           0.85      2026
   macro avg       0.92      0.50      0.46      2026
weighted avg       0.87      0.85      0.78      2026

• In the context of XGBoost, the F-score refers to the feature importance score calculated for each feature. The F-score is a measure of the total reduction in the impurity of the model when splitting on a particular feature. It is used to rank the importance of features in the model and can be used to identify the most relevant features for prediction. Features with higher F-scores are considered more important to the model.

# Create the DMatrix
dmatrix = xgb.DMatrix(data=X_encoded, label=y)

# Create the parameter dictionary: params
params = {"objective": "binary:logistic", "max_depth": 2}

# Train the model: xg_reg
xg_reg = xgb.train(params=params, dtrain=dmatrix, num_boost_round=10)

# Plot the first tree
fig = plt.figure(figsize=(25, 20), dpi=300)
xgb.plot_tree(xg_reg, num_trees=0, ax=plt.gca(), fontsize=14)
plt.show()

# Plot the fifth tree
fig = plt.figure(figsize=(25, 20), dpi=300)
xgb.plot_tree(xg_reg, num_trees=4, ax=plt.gca(), fontsize=14)
plt.show()

Economics

# Plot distribution of total transaction count by attrition flag
plt.figure(figsize=(10, 6))
sns.kdeplot(data=df_clean,
            x='total_trans_ct',
            hue='churned',
            fill=True,
            alpha=0.5,
            palette='cool')
plt.title('Distribution of Total Transaction Count by Attrition Flag')
plt.xlabel('Total Transaction Count')
plt.ylabel('Density')
plt.show()

churned_trans_by_gender_age = df_clean[df_clean['churned'] == 1].groupby(
    ['gender'])['total_trans_ct'].describe()
pd.set_option('max_colwidth', 20)
pd.set_option('display.max_columns', 10)
churned_trans_by_gender_age

	count	mean	std	min	25%	50%	75%	max
gender
Male	697.0	46.110473	17.437841	10.0	35.0	44.0	57.0	91.0
Female	930.0	44.051613	11.909848	12.0	38.0	43.0	49.0	94.0

# Plot distribution of total revolving balance by attrition flag
plt.figure(figsize=(10, 6), dpi=250)
sns.kdeplot(data=df_clean,
            x='total_revolving_bal',
            hue='churned',
            fill=True,
            alpha=0.5,
            palette='cool')
plt.title('Distribution of Total Revolving Balance by Attrition Flag')
plt.xlabel('Total Revolving Balance')
plt.ylabel('Density')
plt.show()

churned_bal_by_gender_age = df_clean[df_clean['churned'] == 1].groupby(
    ['gender'])['total_revolving_bal'].describe()
pd.set_option('max_colwidth', 20)
pd.set_option('display.max_columns', 10)
churned_bal_by_gender_age

	count	mean	std	min	25%	50%	75%	max
gender
Male	697.0	680.315638	917.519035	0.0	0.0	0.0	1307.00	2517.0
Female	930.0	667.207527	924.726152	0.0	0.0	0.0	1298.25	2517.0

# Plot distribution of total relationship count by attrition flag
plt.figure(figsize=(10, 6), dpi=250)
sns.kdeplot(data=df_clean,
            x='total_relationship_count',
            hue='churned',
            fill=True,
            alpha=0.5,
            palette='cool')
plt.title('Distribution of Total Relationship Count by Attrition Flag')
plt.xlabel('Total Relationship Count')
plt.ylabel('Density')
plt.show()

churned_count_by_relationship = df_clean[df_clean['churned'] == 1].groupby(
    ['total_relationship_count'])['clientnum'].count()
churned_count_by_relationship.describe()

count      6.000000
mean     271.166667
std       81.715156
min      196.000000
25%      225.500000
50%      230.000000
75%      317.750000
max      400.000000
Name: clientnum, dtype: float64

# Plot distribution of total transaction amount by attrition flag
plt.figure(figsize=(10, 6), dpi=250)
sns.kdeplot(data=df_clean,
            x='total_trans_amt',
            hue='churned',
            fill=True,
            alpha=0.5,
            palette='cool')
plt.title('Distribution of Total Transaction Amount by Attrition Flag')
plt.xlabel('Total Transaction Amount')
plt.ylabel('Density')
plt.show()

churned_trans_by_gender_age = df_clean[df_clean['churned'] == 1].groupby(
    ['gender'])['total_trans_amt'].describe()

pd.set_option('max_colwidth', 20)
pd.set_option('display.max_columns', 10)
churned_trans_by_gender_age

	count	mean	std	min	25%	50%	75%	max
gender
Male	697.0	3509.779053	2766.604342	530.0	1759.00	2270.0	4883.00	10583.0
Female	930.0	2784.183871	1834.421181	510.0	2013.25	2360.0	2645.75	10294.0

# Plot distribution of total count change by attrition flag
plt.figure(figsize=(10, 6))
sns.kdeplot(data=df_clean,
            x='total_ct_chng_q4_q1',
            hue='churned',
            fill=True,
            alpha=0.5,
            palette='cool')
plt.title('Distribution of Total Count Change by Attrition Flag')
plt.xlabel('Total Count Change Q4-Q1')
plt.ylabel('Density')
plt.show()

churned_total_ct_chng_q4_q1_by_gender = df_clean[
    df_clean['churned'] == 1].groupby(['gender'
                                       ])['total_ct_chng_q4_q1'].describe()
pd.set_option('max_colwidth', 20)
pd.set_option('display.max_columns', 10)
churned_total_ct_chng_q4_q1_by_gender

	count	mean	std	min	25%	50%	75%	max
gender
Male	697.0	0.579077	0.252603	0.0	0.4	0.552	0.735	2.500
Female	930.0	0.535881	0.203639	0.0	0.4	0.517	0.654	1.684

Client Activity

# Set style
sns.set_style('whitegrid')

# Create distribution plot of contacts count by attrition flag
plt.figure(figsize=(10, 6))
sns.kdeplot(data=df_clean,
            x='contacts_count_12_mon',
            hue='churned',
            fill=True,
            alpha=0.5,
            palette='cool')
plt.title(
    'Distribution of Contacts Count in the Last 12 Months by Attrition Flag')
plt.xlabel('Contacts Count in the Last 12 Months')
plt.ylabel('Density')
plt.show()

# Import libraries
import matplotlib.pyplot as plt
import seaborn as sns

# Set style
sns.set_style('whitegrid')

# Create histogram of contacts count by attrition flag
plt.figure(figsize=(10, 6))
sns.histplot(data=df_clean,
            x='contacts_count_12_mon',
            hue='churned',
            multiple='stack',
            palette='cool')
plt.title('Distribution of Contacts Count in the Last 12 Months by Attrition Flag')
plt.xlabel('Contacts Count in the Last 12 Months')
plt.ylabel('Count')
plt.show()

# Plot distribution of months on book by attrition flag
plt.figure(figsize=(10, 6))
sns.kdeplot(data=df_clean,
            x='months_on_book',
            hue='churned',
            fill=True,
            alpha=0.5,
            palette='cool')
plt.title('Distribution of Months on Book by Attrition Flag')
plt.xlabel('Months on Book')
plt.ylabel('Density')
plt.show()

• Customer retention: Since the mean number of months on the book is 36, we can assume that a significant proportion of the customers have been with the bank for a relatively long period of time. This could indicate that the bank has been successful in retaining its customers over a longer period of time.